%---------------------- %1）读低温黑体数据 filename="低温黑体原始图像数据.raw"; if ~exist (filename,'file') errordlg('文件不存在'); return end if ~isequal(filename(end-3:end),'.raw') errordlg('文件格式必须是Raw'); return end numRows=640; numCols=512; Num=numRow...-Go服务器开发

以下是将MATLAB代码转换为C++代码的示例：

#include <iostream>
#include <fstream>
#include <vector>

const int numRows = 640;
const int numCols = 512;

void readRawData(const std::string& filename, std::vector<std::vector<uint16_t>>& data) {
    std::ifstream file(filename, std::ios::binary);
    if (!file) {
        throw std::runtime_error("文件不存在");
    }
    
    file.seekg(0, std::ios_base::end);
    size_t fileSize = file.tellg();
    
    const size_t Num = numRows * numCols;
    const size_t L_frame = fileSize / (2 * Num);
    
    if (L_frame <= 0) {
        throw std::runtime_error("图像大小不够一张！");
    }
    
    const size_t L = std::min<size_t>(100, L_frame); // 控制读取的帧数
    
    data.resize(L, std::vector<uint16_t>(Num));
    
    for (size_t i = 0; i < L; ++i) {
        file.seekg(2 * Num * i, std::ios_base::beg);
        file.read(reinterpret_cast<char*>(data[i].data()), Num * sizeof(uint16_t));
        
        if (!file) {
            throw std::runtime_error("读取文件失败");
        }
    }
    
    file.close();
}

void calculateKAndB(const std::vector<std::vector<uint16_t>>& T0_data,
                    const std::vector<std::vector<uint16_t>>& T1_data,
                    double& K,
                    double& B) {
    const size_t Num = numRows * numCols;
    
    double T0_avg = 0.0;
    double T1_avg = 0.0;
    size_t L = T0_data.size();
    
    for (size_t i = 0; i < L; ++i) {
        for (size_t j = 0; j < Num; ++j) {
            T0_avg += T0_data[i][j];
            T1_avg += T1_data[i][j];
        }
    }
    
    T0_avg /= (L * Num);
    T1_avg /= (L * Num);
    
    double sum_T0_diff = 0.0;
    double sum_T1_diff = 0.0;
    
    for (size_t i = 0; i < L; ++i) {
        for (size_t j = 0; j < Num; ++j) {
            sum_T0_diff += std::pow(T0_data[i][j] - T0_avg, 2);
            sum_T1_diff += std::pow(T1_data[i][j] - T1_avg, 2);
        }
    }
    
    double var_T0 = sum_T0_diff / (L * Num);
    double var_T1 = sum_T1_diff / (L * Num);
    
    K = (T1_avg - T1dn) / (T0_avg - T1_avg);
    K = std::max(0.0, K);
    
    B = ((T1dn * T0_avg) - (T0_DN * T1_avg)) / (T_10avg - t_20avg);
}

void correction(const std::vector<uint16_t>& input,
                const double& K,
                const double& B,
                std::vector<uint16_t>& output) {
    output.resize(input.size());
    
    for (size_t i = 0; i < input.size(); ++i) {
        output[i] = static_cast<uint16_t>((K * input[i]) + B);
    }
}

int main() {
    std::vector<std::vector<uint16_t>> T0_data, T1_data;
    std::vector<uint16_t> originalImageData;
    
    try {
        readRawData("低温黑体原始图像数据.raw", T0_data);
        readRawData("高温黑体原始图像数据.raw", T1_data);
        readRawData("校正的原始图像数据.raw", originalImageData);
        
        double K, B;
        calculateKAndB(T0_data, T1_data, K, B);
        
        std::vector<uint16_t> correctedImageData;
        correction(originalImageData, K, B, correctedImageData);
        
        // 在这里可以处理校正后的图像数据
        
    } catch (const std::exception& e) {
        std::cerr << "错误: " << e.what() << std::endl;
    }
    
    return 0;
}

请注意，此处只提供了代码转换示例，并不包括完整的实现细节。你可能需要根据具体需求进行适当的修改和优化。

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